On QFT tuning of multivariable μ controllers

Optimal control involves feedback problems with explicit plant data and performance criteria for which a solution is either synthesized or ruled out. H¥ optimal control is probably the most renowned technique in this class where the control synthesis procedure involves various iterations over weightings. In this paper we argue that the integration of optimal control synthesis and manual tuning in the Quantitative Feedback Theory (QFT) design environment enables design of controllers with levels of performance that surpasses what can be achieved using only a single technique. Specifically, using a constructive example, we demonstrate that QFT's open-loop tuning is can be more transparent than tuning closed-loop weights. In this example, QFT tunes the m controller with the objective of reducing control bandwidth while maintaining robust performance (m <1).